Overview

Dataset statistics

Number of variables14
Number of observations8760
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory336.8 B

Variable types

Categorical3
Numeric10
Boolean1

Alerts

Date has a high cardinality: 365 distinct valuesHigh cardinality
Rented Bike Count is highly overall correlated with Temperature(�C)High correlation
Temperature(�C) is highly overall correlated with Rented Bike Count and 2 other fieldsHigh correlation
Humidity(%) is highly overall correlated with Dew point temperature(�C)High correlation
Dew point temperature(�C) is highly overall correlated with Temperature(�C) and 2 other fieldsHigh correlation
Seasons is highly overall correlated with Temperature(�C) and 1 other fieldsHigh correlation
Holiday is highly imbalanced (71.7%)Imbalance
Functioning Day is highly imbalanced (78.7%)Imbalance
Date is uniformly distributedUniform
Rented Bike Count has 295 (3.4%) zerosZeros
Hour has 365 (4.2%) zerosZeros
Solar Radiation (MJ/m2) has 4300 (49.1%) zerosZeros
Rainfall(mm) has 8232 (94.0%) zerosZeros
Snowfall (cm) has 8317 (94.9%) zerosZeros

Reproduction

Analysis started2023-06-28 17:09:16.132774
Analysis finished2023-06-28 17:09:49.708490
Duration33.58 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct365
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size573.3 KiB
01/12/2017
 
24
09/08/2018
 
24
07/08/2018
 
24
06/08/2018
 
24
05/08/2018
 
24
Other values (360)
8640 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters87600
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01/12/2017
2nd row01/12/2017
3rd row01/12/2017
4th row01/12/2017
5th row01/12/2017

Common Values

ValueCountFrequency (%)
01/12/2017 24
 
0.3%
09/08/2018 24
 
0.3%
07/08/2018 24
 
0.3%
06/08/2018 24
 
0.3%
05/08/2018 24
 
0.3%
04/08/2018 24
 
0.3%
03/08/2018 24
 
0.3%
02/08/2018 24
 
0.3%
01/08/2018 24
 
0.3%
31/07/2018 24
 
0.3%
Other values (355) 8520
97.3%

Length

2023-06-28T17:09:49.862540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01/12/2017 24
 
0.3%
24/12/2017 24
 
0.3%
03/12/2017 24
 
0.3%
04/12/2017 24
 
0.3%
05/12/2017 24
 
0.3%
06/12/2017 24
 
0.3%
07/12/2017 24
 
0.3%
08/12/2017 24
 
0.3%
09/12/2017 24
 
0.3%
10/12/2017 24
 
0.3%
Other values (355) 8520
97.3%

Most occurring characters

ValueCountFrequency (%)
0 19488
22.2%
/ 17520
20.0%
1 16344
18.7%
2 13896
15.9%
8 9624
11.0%
7 2352
 
2.7%
3 2040
 
2.3%
5 1608
 
1.8%
6 1584
 
1.8%
4 1584
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70080
80.0%
Other Punctuation 17520
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19488
27.8%
1 16344
23.3%
2 13896
19.8%
8 9624
13.7%
7 2352
 
3.4%
3 2040
 
2.9%
5 1608
 
2.3%
6 1584
 
2.3%
4 1584
 
2.3%
9 1560
 
2.2%
Other Punctuation
ValueCountFrequency (%)
/ 17520
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 87600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19488
22.2%
/ 17520
20.0%
1 16344
18.7%
2 13896
15.9%
8 9624
11.0%
7 2352
 
2.7%
3 2040
 
2.3%
5 1608
 
1.8%
6 1584
 
1.8%
4 1584
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19488
22.2%
/ 17520
20.0%
1 16344
18.7%
2 13896
15.9%
8 9624
11.0%
7 2352
 
2.7%
3 2040
 
2.3%
5 1608
 
1.8%
6 1584
 
1.8%
4 1584
 
1.8%

Rented Bike Count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2166
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean704.60205
Minimum0
Maximum3556
Zeros295
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-06-28T17:09:50.146440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q1191
median504.5
Q31065.25
95-th percentile2043
Maximum3556
Range3556
Interquartile range (IQR)874.25

Descriptive statistics

Standard deviation644.99747
Coefficient of variation (CV)0.91540674
Kurtosis0.85338699
Mean704.60205
Median Absolute Deviation (MAD)373.5
Skewness1.1534282
Sum6172314
Variance416021.73
MonotonicityNot monotonic
2023-06-28T17:09:50.429282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 295
 
3.4%
122 19
 
0.2%
223 19
 
0.2%
262 19
 
0.2%
165 18
 
0.2%
103 18
 
0.2%
189 18
 
0.2%
178 17
 
0.2%
170 17
 
0.2%
71 17
 
0.2%
Other values (2156) 8303
94.8%
ValueCountFrequency (%)
0 295
3.4%
2 3
 
< 0.1%
3 2
 
< 0.1%
4 5
 
0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 7
 
0.1%
9 12
 
0.1%
10 7
 
0.1%
ValueCountFrequency (%)
3556 1
< 0.1%
3418 1
< 0.1%
3404 1
< 0.1%
3384 1
< 0.1%
3380 1
< 0.1%
3365 1
< 0.1%
3309 1
< 0.1%
3298 1
< 0.1%
3277 1
< 0.1%
3256 1
< 0.1%

Hour
Real number (ℝ)

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros365
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-06-28T17:09:50.692539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9225817
Coefficient of variation (CV)0.60196363
Kurtosis-1.2041763
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum100740
Variance47.922137
MonotonicityNot monotonic
2023-06-28T17:09:50.925896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 365
 
4.2%
1 365
 
4.2%
22 365
 
4.2%
21 365
 
4.2%
20 365
 
4.2%
19 365
 
4.2%
18 365
 
4.2%
17 365
 
4.2%
16 365
 
4.2%
15 365
 
4.2%
Other values (14) 5110
58.3%
ValueCountFrequency (%)
0 365
4.2%
1 365
4.2%
2 365
4.2%
3 365
4.2%
4 365
4.2%
5 365
4.2%
6 365
4.2%
7 365
4.2%
8 365
4.2%
9 365
4.2%
ValueCountFrequency (%)
23 365
4.2%
22 365
4.2%
21 365
4.2%
20 365
4.2%
19 365
4.2%
18 365
4.2%
17 365
4.2%
16 365
4.2%
15 365
4.2%
14 365
4.2%

Temperature(�C)
Real number (ℝ)

Distinct546
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.882922
Minimum-17.8
Maximum39.4
Zeros21
Zeros (%)0.2%
Negative1433
Negative (%)16.4%
Memory size68.6 KiB
2023-06-28T17:09:51.190130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-17.8
5-th percentile-7.1
Q13.5
median13.7
Q322.5
95-th percentile30.7
Maximum39.4
Range57.2
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.944825
Coefficient of variation (CV)0.92718289
Kurtosis-0.83778629
Mean12.882922
Median Absolute Deviation (MAD)9.4
Skewness-0.19832553
Sum112854.4
Variance142.67885
MonotonicityNot monotonic
2023-06-28T17:09:51.489514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.1 40
 
0.5%
20.5 40
 
0.5%
23.4 39
 
0.4%
7.6 38
 
0.4%
20.7 38
 
0.4%
24.2 37
 
0.4%
20.2 35
 
0.4%
19.4 34
 
0.4%
19 34
 
0.4%
18.8 33
 
0.4%
Other values (536) 8392
95.8%
ValueCountFrequency (%)
-17.8 1
 
< 0.1%
-17.5 2
 
< 0.1%
-17.4 1
 
< 0.1%
-16.9 1
 
< 0.1%
-16.5 1
 
< 0.1%
-16.4 2
 
< 0.1%
-16.2 3
< 0.1%
-16.1 2
 
< 0.1%
-16 2
 
< 0.1%
-15.9 5
0.1%
ValueCountFrequency (%)
39.4 1
 
< 0.1%
39.3 1
 
< 0.1%
39 1
 
< 0.1%
38.7 1
 
< 0.1%
38 1
 
< 0.1%
37.9 2
 
< 0.1%
37.8 3
< 0.1%
37.6 1
 
< 0.1%
37.5 1
 
< 0.1%
37.4 6
0.1%

Humidity(%)
Real number (ℝ)

Distinct90
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.226256
Minimum0
Maximum98
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-06-28T17:09:51.786675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27
Q142
median57
Q374
95-th percentile94
Maximum98
Range98
Interquartile range (IQR)32

Descriptive statistics

Standard deviation20.362413
Coefficient of variation (CV)0.34971188
Kurtosis-0.80355919
Mean58.226256
Median Absolute Deviation (MAD)16
Skewness0.059578973
Sum510062
Variance414.62788
MonotonicityNot monotonic
2023-06-28T17:09:52.083873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 173
 
2.0%
97 173
 
2.0%
43 164
 
1.9%
57 159
 
1.8%
56 157
 
1.8%
47 156
 
1.8%
51 155
 
1.8%
63 153
 
1.7%
54 151
 
1.7%
52 150
 
1.7%
Other values (80) 7169
81.8%
ValueCountFrequency (%)
0 17
0.2%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
13 3
 
< 0.1%
14 16
0.2%
15 17
0.2%
16 15
0.2%
17 21
0.2%
18 15
0.2%
ValueCountFrequency (%)
98 50
 
0.6%
97 173
2.0%
96 111
1.3%
95 68
 
0.8%
94 54
 
0.6%
93 38
 
0.4%
92 27
 
0.3%
91 38
 
0.4%
90 52
 
0.6%
89 62
 
0.7%

Wind speed (m/s)
Real number (ℝ)

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7249087
Minimum0
Maximum7.4
Zeros74
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-06-28T17:09:52.387054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q10.9
median1.5
Q32.3
95-th percentile3.7
Maximum7.4
Range7.4
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.0363
Coefficient of variation (CV)0.60078543
Kurtosis0.72717945
Mean1.7249087
Median Absolute Deviation (MAD)0.7
Skewness0.8909548
Sum15110.2
Variance1.0739177
MonotonicityNot monotonic
2023-06-28T17:09:52.668980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 420
 
4.8%
1.2 403
 
4.6%
1 388
 
4.4%
0.9 388
 
4.4%
0.8 385
 
4.4%
1.4 355
 
4.1%
1.3 344
 
3.9%
1.5 343
 
3.9%
1.6 332
 
3.8%
0.6 321
 
3.7%
Other values (55) 5081
58.0%
ValueCountFrequency (%)
0 74
 
0.8%
0.1 49
 
0.6%
0.2 86
 
1.0%
0.3 158
1.8%
0.4 186
2.1%
0.5 258
2.9%
0.6 321
3.7%
0.7 313
3.6%
0.8 385
4.4%
0.9 388
4.4%
ValueCountFrequency (%)
7.4 1
 
< 0.1%
7.3 1
 
< 0.1%
7.2 1
 
< 0.1%
6.9 1
 
< 0.1%
6.7 1
 
< 0.1%
6.1 1
 
< 0.1%
6 2
< 0.1%
5.8 4
< 0.1%
5.7 1
 
< 0.1%
5.6 2
< 0.1%

Visibility (10m)
Real number (ℝ)

Distinct1789
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1436.8258
Minimum27
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-06-28T17:09:52.967148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile300
Q1940
median1698
Q32000
95-th percentile2000
Maximum2000
Range1973
Interquartile range (IQR)1060

Descriptive statistics

Standard deviation608.29871
Coefficient of variation (CV)0.42336288
Kurtosis-0.96198013
Mean1436.8258
Median Absolute Deviation (MAD)302
Skewness-0.70178645
Sum12586594
Variance370027.32
MonotonicityNot monotonic
2023-06-28T17:09:53.263723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 2245
 
25.6%
1995 34
 
0.4%
1985 28
 
0.3%
1999 28
 
0.3%
1989 28
 
0.3%
1996 27
 
0.3%
1992 26
 
0.3%
1998 25
 
0.3%
1981 23
 
0.3%
1987 23
 
0.3%
Other values (1779) 6273
71.6%
ValueCountFrequency (%)
27 1
< 0.1%
33 1
< 0.1%
34 1
< 0.1%
38 1
< 0.1%
53 1
< 0.1%
54 1
< 0.1%
59 1
< 0.1%
63 1
< 0.1%
66 2
< 0.1%
70 1
< 0.1%
ValueCountFrequency (%)
2000 2245
25.6%
1999 28
 
0.3%
1998 25
 
0.3%
1997 22
 
0.3%
1996 27
 
0.3%
1995 34
 
0.4%
1994 18
 
0.2%
1993 13
 
0.1%
1992 26
 
0.3%
1991 14
 
0.2%
Distinct556
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0738128
Minimum-30.6
Maximum27.2
Zeros60
Zeros (%)0.7%
Negative3138
Negative (%)35.8%
Memory size68.6 KiB
2023-06-28T17:09:53.595854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-30.6
5-th percentile-19.505
Q1-4.7
median5.1
Q314.8
95-th percentile22.405
Maximum27.2
Range57.8
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation13.060369
Coefficient of variation (CV)3.2059326
Kurtosis-0.75542951
Mean4.0738128
Median Absolute Deviation (MAD)9.7
Skewness-0.36729844
Sum35686.6
Variance170.57325
MonotonicityNot monotonic
2023-06-28T17:09:53.918865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
 
0.7%
21.1 43
 
0.5%
14.3 40
 
0.5%
21.2 40
 
0.5%
8.9 39
 
0.4%
21.8 39
 
0.4%
2.2 38
 
0.4%
21.3 38
 
0.4%
20.2 37
 
0.4%
21.5 36
 
0.4%
Other values (546) 8350
95.3%
ValueCountFrequency (%)
-30.6 1
< 0.1%
-30.5 1
< 0.1%
-29.8 1
< 0.1%
-29.7 1
< 0.1%
-29.6 2
< 0.1%
-29.5 1
< 0.1%
-29.2 1
< 0.1%
-29.1 1
< 0.1%
-29 2
< 0.1%
-28.9 2
< 0.1%
ValueCountFrequency (%)
27.2 1
 
< 0.1%
26.8 2
< 0.1%
26.6 1
 
< 0.1%
26.3 1
 
< 0.1%
26.1 3
< 0.1%
26 2
< 0.1%
25.9 1
 
< 0.1%
25.8 2
< 0.1%
25.7 1
 
< 0.1%
25.6 2
< 0.1%

Solar Radiation (MJ/m2)
Real number (ℝ)

Distinct345
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56911073
Minimum0
Maximum3.52
Zeros4300
Zeros (%)49.1%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-06-28T17:09:54.212453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.93
95-th percentile2.56
Maximum3.52
Range3.52
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation0.86874624
Coefficient of variation (CV)1.5264977
Kurtosis1.126433
Mean0.56911073
Median Absolute Deviation (MAD)0.01
Skewness1.5040397
Sum4985.41
Variance0.75472003
MonotonicityNot monotonic
2023-06-28T17:09:54.507662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4300
49.1%
0.01 128
 
1.5%
0.02 82
 
0.9%
0.03 69
 
0.8%
0.06 61
 
0.7%
0.05 54
 
0.6%
0.04 47
 
0.5%
0.11 44
 
0.5%
0.07 37
 
0.4%
0.16 36
 
0.4%
Other values (335) 3902
44.5%
ValueCountFrequency (%)
0 4300
49.1%
0.01 128
 
1.5%
0.02 82
 
0.9%
0.03 69
 
0.8%
0.04 47
 
0.5%
0.05 54
 
0.6%
0.06 61
 
0.7%
0.07 37
 
0.4%
0.08 33
 
0.4%
0.09 32
 
0.4%
ValueCountFrequency (%)
3.52 2
< 0.1%
3.49 1
 
< 0.1%
3.45 1
 
< 0.1%
3.44 1
 
< 0.1%
3.42 4
< 0.1%
3.41 2
< 0.1%
3.39 3
< 0.1%
3.38 1
 
< 0.1%
3.36 4
< 0.1%
3.35 1
 
< 0.1%

Rainfall(mm)
Real number (ℝ)

Distinct61
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14868721
Minimum0
Maximum35
Zeros8232
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-06-28T17:09:55.151047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum35
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.128193
Coefficient of variation (CV)7.5876932
Kurtosis284.9911
Mean0.14868721
Median Absolute Deviation (MAD)0
Skewness14.533232
Sum1302.5
Variance1.2728194
MonotonicityNot monotonic
2023-06-28T17:09:55.437700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8232
94.0%
0.5 116
 
1.3%
1 66
 
0.8%
1.5 56
 
0.6%
0.1 46
 
0.5%
2 31
 
0.4%
2.5 23
 
0.3%
0.2 20
 
0.2%
3.5 18
 
0.2%
0.4 16
 
0.2%
Other values (51) 136
 
1.6%
ValueCountFrequency (%)
0 8232
94.0%
0.1 46
 
0.5%
0.2 20
 
0.2%
0.3 9
 
0.1%
0.4 16
 
0.2%
0.5 116
 
1.3%
0.7 1
 
< 0.1%
0.8 3
 
< 0.1%
0.9 3
 
< 0.1%
1 66
 
0.8%
ValueCountFrequency (%)
35 1
< 0.1%
29.5 1
< 0.1%
24 1
< 0.1%
21.5 1
< 0.1%
21 1
< 0.1%
19 1
< 0.1%
18.5 2
< 0.1%
18 2
< 0.1%
17 1
< 0.1%
16 1
< 0.1%

Snowfall (cm)
Real number (ℝ)

Distinct51
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075068493
Minimum0
Maximum8.8
Zeros8317
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size68.6 KiB
2023-06-28T17:09:55.731668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.2
Maximum8.8
Range8.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43674618
Coefficient of variation (CV)5.8179692
Kurtosis93.803324
Mean0.075068493
Median Absolute Deviation (MAD)0
Skewness8.4408008
Sum657.6
Variance0.19074723
MonotonicityNot monotonic
2023-06-28T17:09:55.999000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8317
94.9%
0.3 42
 
0.5%
1 39
 
0.4%
0.9 34
 
0.4%
0.5 34
 
0.4%
0.7 31
 
0.4%
0.8 22
 
0.3%
2 22
 
0.3%
0.4 21
 
0.2%
1.6 19
 
0.2%
Other values (41) 179
 
2.0%
ValueCountFrequency (%)
0 8317
94.9%
0.1 2
 
< 0.1%
0.2 15
 
0.2%
0.3 42
 
0.5%
0.4 21
 
0.2%
0.5 34
 
0.4%
0.6 15
 
0.2%
0.7 31
 
0.4%
0.8 22
 
0.3%
0.9 34
 
0.4%
ValueCountFrequency (%)
8.8 2
< 0.1%
7.1 1
 
< 0.1%
7 1
 
< 0.1%
6 1
 
< 0.1%
5.1 1
 
< 0.1%
5 2
< 0.1%
4.8 2
< 0.1%
4.3 2
< 0.1%
4.2 1
 
< 0.1%
4.1 4
< 0.1%

Seasons
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size539.1 KiB
Spring
2208 
Summer
2208 
Autumn
2184 
Winter
2160 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters52560
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWinter
2nd rowWinter
3rd rowWinter
4th rowWinter
5th rowWinter

Common Values

ValueCountFrequency (%)
Spring 2208
25.2%
Summer 2208
25.2%
Autumn 2184
24.9%
Winter 2160
24.7%

Length

2023-06-28T17:09:56.244799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T17:09:56.546602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
spring 2208
25.2%
summer 2208
25.2%
autumn 2184
24.9%
winter 2160
24.7%

Most occurring characters

ValueCountFrequency (%)
m 6600
12.6%
r 6576
12.5%
u 6576
12.5%
n 6552
12.5%
S 4416
8.4%
i 4368
8.3%
e 4368
8.3%
t 4344
8.3%
p 2208
 
4.2%
g 2208
 
4.2%
Other values (2) 4344
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43800
83.3%
Uppercase Letter 8760
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 6600
15.1%
r 6576
15.0%
u 6576
15.0%
n 6552
15.0%
i 4368
10.0%
e 4368
10.0%
t 4344
9.9%
p 2208
 
5.0%
g 2208
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
S 4416
50.4%
A 2184
24.9%
W 2160
24.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 52560
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 6600
12.6%
r 6576
12.5%
u 6576
12.5%
n 6552
12.5%
S 4416
8.4%
i 4368
8.3%
e 4368
8.3%
t 4344
8.3%
p 2208
 
4.2%
g 2208
 
4.2%
Other values (2) 4344
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 6600
12.6%
r 6576
12.5%
u 6576
12.5%
n 6552
12.5%
S 4416
8.4%
i 4368
8.3%
e 4368
8.3%
t 4344
8.3%
p 2208
 
4.2%
g 2208
 
4.2%
Other values (2) 4344
8.3%

Holiday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size572.0 KiB
No Holiday
8328 
Holiday
 
432

Length

Max length10
Median length10
Mean length9.8520548
Min length7

Characters and Unicode

Total characters86304
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Holiday
2nd rowNo Holiday
3rd rowNo Holiday
4th rowNo Holiday
5th rowNo Holiday

Common Values

ValueCountFrequency (%)
No Holiday 8328
95.1%
Holiday 432
 
4.9%

Length

2023-06-28T17:09:56.785423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T17:09:57.038080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
holiday 8760
51.3%
no 8328
48.7%

Most occurring characters

ValueCountFrequency (%)
o 17088
19.8%
H 8760
10.2%
l 8760
10.2%
i 8760
10.2%
d 8760
10.2%
a 8760
10.2%
y 8760
10.2%
N 8328
9.6%
8328
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60888
70.6%
Uppercase Letter 17088
 
19.8%
Space Separator 8328
 
9.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 17088
28.1%
l 8760
14.4%
i 8760
14.4%
d 8760
14.4%
a 8760
14.4%
y 8760
14.4%
Uppercase Letter
ValueCountFrequency (%)
H 8760
51.3%
N 8328
48.7%
Space Separator
ValueCountFrequency (%)
8328
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 77976
90.4%
Common 8328
 
9.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 17088
21.9%
H 8760
11.2%
l 8760
11.2%
i 8760
11.2%
d 8760
11.2%
a 8760
11.2%
y 8760
11.2%
N 8328
10.7%
Common
ValueCountFrequency (%)
8328
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 17088
19.8%
H 8760
10.2%
l 8760
10.2%
i 8760
10.2%
d 8760
10.2%
a 8760
10.2%
y 8760
10.2%
N 8328
9.6%
8328
9.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.7 KiB
True
8465 
False
 
295
ValueCountFrequency (%)
True 8465
96.6%
False 295
 
3.4%
2023-06-28T17:09:57.274962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-06-28T17:09:45.595140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:19.978414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:22.810658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:25.594501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:28.092354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:31.002563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:34.687947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:37.238836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:39.725120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:42.271384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:45.985574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:20.279877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:23.157165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:25.846075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:28.336683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:31.448130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:34.952695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:37.493826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:39.982927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:42.522216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:46.343551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:20.511053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:23.477860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:26.067912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:28.574955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:31.823947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:35.192795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:37.717594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:40.221317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:42.762886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:46.707629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:20.771867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:23.708446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:26.310278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:28.812918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:32.174051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:35.451158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:37.966531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:40.464884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:43.032502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:47.117623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:21.013848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:23.942134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:26.559368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:29.036921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:32.558600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:35.697759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:38.203053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:40.708218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:43.550678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:47.484489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:21.294518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:24.184358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:26.813767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:29.282103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:32.921843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:35.965894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:38.455535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:40.985137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:43.798995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:47.853879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:21.554929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:24.621250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:27.085629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:29.549027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:33.302116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:36.225582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:38.714369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:41.253382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:44.105481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:48.089422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:21.817872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:24.870066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:27.362338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:29.793324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:33.920975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:36.472987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:38.979985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:41.514113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:44.476635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:48.342201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:22.167853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:25.131262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:27.619897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:30.055852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:34.184378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:36.729913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:39.239579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:41.771223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:44.877915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:48.583690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:22.578028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:25.373834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:27.868648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:30.388363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:34.447080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:36.992831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:39.507238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:42.032990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T17:09:45.275231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-28T17:09:57.470942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Rented Bike CountHourTemperature(�C)Humidity(%)Wind speed (m/s)Visibility (10m)Dew point temperature(�C)Solar Radiation (MJ/m2)Rainfall(mm)Snowfall (cm)SeasonsHolidayFunctioning Day
Rented Bike Count1.0000.3890.565-0.2210.1480.1760.3740.382-0.282-0.2210.3130.0990.219
Hour0.3891.0000.121-0.2510.3070.0940.0030.209-0.026-0.0320.0000.0000.000
Temperature(�C)0.5650.1211.0000.1540.0110.0460.9120.3280.072-0.3070.6420.1460.195
Humidity(%)-0.221-0.2510.1541.000-0.355-0.4830.521-0.4380.3680.0500.1840.0790.068
Wind speed (m/s)0.1480.3070.011-0.3551.0000.154-0.1280.363-0.0520.0290.1100.0470.000
Visibility (10m)0.1760.0940.046-0.4830.1541.000-0.1290.049-0.232-0.0740.1360.0760.033
Dew point temperature(�C)0.3740.0030.9120.521-0.128-0.1291.0000.0940.213-0.2490.6180.1180.232
Solar Radiation (MJ/m2)0.3820.2090.328-0.4380.3630.0490.0941.000-0.091-0.0770.1330.0000.020
Rainfall(mm)-0.282-0.0260.0720.368-0.052-0.2320.213-0.0911.0000.0020.0260.0000.000
Snowfall (cm)-0.221-0.032-0.3070.0500.029-0.074-0.249-0.0770.0021.0000.1510.0220.007
Seasons0.3130.0000.6420.1840.1100.1360.6180.1330.0260.1511.0000.1170.258
Holiday0.0990.0000.1460.0790.0470.0760.1180.0000.0000.0220.1171.0000.024
Functioning Day0.2190.0000.1950.0680.0000.0330.2320.0200.0000.0070.2580.0241.000

Missing values

2023-06-28T17:09:48.929994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-28T17:09:49.450769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateRented Bike CountHourTemperature(�C)Humidity(%)Wind speed (m/s)Visibility (10m)Dew point temperature(�C)Solar Radiation (MJ/m2)Rainfall(mm)Snowfall (cm)SeasonsHolidayFunctioning Day
001/12/20172540-5.2372.22000-17.60.000.00.0WinterNo HolidayYes
101/12/20172041-5.5380.82000-17.60.000.00.0WinterNo HolidayYes
201/12/20171732-6.0391.02000-17.70.000.00.0WinterNo HolidayYes
301/12/20171073-6.2400.92000-17.60.000.00.0WinterNo HolidayYes
401/12/2017784-6.0362.32000-18.60.000.00.0WinterNo HolidayYes
501/12/20171005-6.4371.52000-18.70.000.00.0WinterNo HolidayYes
601/12/20171816-6.6351.32000-19.50.000.00.0WinterNo HolidayYes
701/12/20174607-7.4380.92000-19.30.000.00.0WinterNo HolidayYes
801/12/20179308-7.6371.12000-19.80.010.00.0WinterNo HolidayYes
901/12/20174909-6.5270.51928-22.40.230.00.0WinterNo HolidayYes
DateRented Bike CountHourTemperature(�C)Humidity(%)Wind speed (m/s)Visibility (10m)Dew point temperature(�C)Solar Radiation (MJ/m2)Rainfall(mm)Snowfall (cm)SeasonsHolidayFunctioning Day
875030/11/2018761147.8202.22000-13.81.670.00.0AutumnNo HolidayYes
875130/11/2018768157.0203.31994-14.41.210.00.0AutumnNo HolidayYes
875230/11/2018837167.2231.51945-12.60.720.00.0AutumnNo HolidayYes
875330/11/20181047176.0292.11877-10.70.230.00.0AutumnNo HolidayYes
875430/11/20181384184.7341.91661-9.80.000.00.0AutumnNo HolidayYes
875530/11/20181003194.2342.61894-10.30.000.00.0AutumnNo HolidayYes
875630/11/2018764203.4372.32000-9.90.000.00.0AutumnNo HolidayYes
875730/11/2018694212.6390.31968-9.90.000.00.0AutumnNo HolidayYes
875830/11/2018712222.1411.01859-9.80.000.00.0AutumnNo HolidayYes
875930/11/2018584231.9431.31909-9.30.000.00.0AutumnNo HolidayYes